Overview

Dataset statistics

Number of variables11
Number of observations3152
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory788.3 KiB
Average record size in memory256.1 B

Variable types

Numeric9
Categorical2

Alerts

state has a high cardinality: 51 distinct valuesHigh cardinality
area_name has a high cardinality: 1886 distinct valuesHigh cardinality
fips is highly overall correlated with stateHigh correlation
2013_urban_influence_code is highly overall correlated with bachelors_degree_or_higher_2015_19High correlation
percent_of_adults_with_a_high_school_diploma_only_1980 is highly overall correlated with percent_of_adults_with_less_than_a_high_school_diploma_2015_19High correlation
percent_of_adults_completing_some_college_or_associates_degree_2000 is highly overall correlated with percent_of_adults_with_less_than_a_high_school_diploma_2015_19 and 3 other fieldsHigh correlation
bachelors_degree_or_higher_2015_19 is highly overall correlated with 2013_urban_influence_code and 1 other fieldsHigh correlation
percent_of_adults_with_less_than_a_high_school_diploma_2015_19 is highly overall correlated with percent_of_adults_with_a_high_school_diploma_only_1980 and 2 other fieldsHigh correlation
percent_of_adults_with_a_high_school_diploma_only_2015_19 is highly overall correlated with percent_of_adults_completing_some_college_or_associates_degree_2000 and 1 other fieldsHigh correlation
percent_of_adults_completing_some_college_or_associates_degree_2015_19 is highly overall correlated with percent_of_adults_completing_some_college_or_associates_degree_2000High correlation
percent_of_adults_with_a_bachelors_degree_or_higher_2015_19 is highly overall correlated with percent_of_adults_completing_some_college_or_associates_degree_2000 and 3 other fieldsHigh correlation
state is highly overall correlated with fipsHigh correlation
fips has unique valuesUnique

Reproduction

Analysis started2023-01-17 13:42:25.936938
Analysis finished2023-01-17 13:45:51.453572
Duration3 minutes and 25.52 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

fips
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct3152
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30334.384
Minimum1001
Maximum56045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2023-01-17T14:45:51.499170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile5080.1
Q118168.5
median29172
Q345079.5
95-th percentile53061.9
Maximum56045
Range55044
Interquartile range (IQR)26911

Descriptive statistics

Standard deviation15205.884
Coefficient of variation (CV)0.50127553
Kurtosis-1.0981241
Mean30334.384
Median Absolute Deviation (MAD)12024
Skewness-0.081457568
Sum95613978
Variance2.3121892 × 108
MonotonicityStrictly increasing
2023-01-17T14:45:51.572040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1001 1
 
< 0.1%
39087 1
 
< 0.1%
39091 1
 
< 0.1%
39093 1
 
< 0.1%
39095 1
 
< 0.1%
39097 1
 
< 0.1%
39099 1
 
< 0.1%
39101 1
 
< 0.1%
39103 1
 
< 0.1%
39105 1
 
< 0.1%
Other values (3142) 3142
99.7%
ValueCountFrequency (%)
1001 1
< 0.1%
1003 1
< 0.1%
1005 1
< 0.1%
1007 1
< 0.1%
1009 1
< 0.1%
1011 1
< 0.1%
1013 1
< 0.1%
1015 1
< 0.1%
1017 1
< 0.1%
1019 1
< 0.1%
ValueCountFrequency (%)
56045 1
< 0.1%
56043 1
< 0.1%
56041 1
< 0.1%
56039 1
< 0.1%
56037 1
< 0.1%
56035 1
< 0.1%
56033 1
< 0.1%
56031 1
< 0.1%
56029 1
< 0.1%
56027 1
< 0.1%

state
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct51
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size181.7 KiB
TX
254 
GA
 
159
VA
 
135
KY
 
120
MO
 
115
Other values (46)
2369 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters6304
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAL
2nd rowAL
3rd rowAL
4th rowAL
5th rowAL

Common Values

ValueCountFrequency (%)
TX 254
 
8.1%
GA 159
 
5.0%
VA 135
 
4.3%
KY 120
 
3.8%
MO 115
 
3.6%
KS 105
 
3.3%
IL 102
 
3.2%
NC 100
 
3.2%
IA 99
 
3.1%
TN 95
 
3.0%
Other values (41) 1868
59.3%

Length

2023-01-17T14:45:51.636193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tx 254
 
8.1%
ga 159
 
5.0%
va 135
 
4.3%
ky 120
 
3.8%
mo 115
 
3.6%
ks 105
 
3.3%
il 102
 
3.2%
nc 100
 
3.2%
ia 99
 
3.1%
tn 95
 
3.0%
Other values (41) 1868
59.3%

Most occurring characters

ValueCountFrequency (%)
A 828
13.1%
N 663
 
10.5%
M 511
 
8.1%
I 502
 
8.0%
T 457
 
7.2%
O 380
 
6.0%
K 338
 
5.4%
L 300
 
4.8%
S 299
 
4.7%
C 277
 
4.4%
Other values (14) 1749
27.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6304
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 828
13.1%
N 663
 
10.5%
M 511
 
8.1%
I 502
 
8.0%
T 457
 
7.2%
O 380
 
6.0%
K 338
 
5.4%
L 300
 
4.8%
S 299
 
4.7%
C 277
 
4.4%
Other values (14) 1749
27.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 6304
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 828
13.1%
N 663
 
10.5%
M 511
 
8.1%
I 502
 
8.0%
T 457
 
7.2%
O 380
 
6.0%
K 338
 
5.4%
L 300
 
4.8%
S 299
 
4.7%
C 277
 
4.4%
Other values (14) 1749
27.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 828
13.1%
N 663
 
10.5%
M 511
 
8.1%
I 502
 
8.0%
T 457
 
7.2%
O 380
 
6.0%
K 338
 
5.4%
L 300
 
4.8%
S 299
 
4.7%
C 277
 
4.4%
Other values (14) 1749
27.7%

area_name
Categorical

Distinct1886
Distinct (%)59.8%
Missing0
Missing (%)0.0%
Memory size218.9 KiB
Washington County
 
30
Jefferson County
 
25
Franklin County
 
24
Lincoln County
 
23
Jackson County
 
23
Other values (1881)
3027 

Length

Max length43
Median length33
Mean length14.061231
Min length10

Characters and Unicode

Total characters44321
Distinct characters55
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1462 ?
Unique (%)46.4%

Sample

1st rowAutauga County
2nd rowBaldwin County
3rd rowBarbour County
4th rowBibb County
5th rowBlount County

Common Values

ValueCountFrequency (%)
Washington County 30
 
1.0%
Jefferson County 25
 
0.8%
Franklin County 24
 
0.8%
Lincoln County 23
 
0.7%
Jackson County 23
 
0.7%
Madison County 19
 
0.6%
Clay County 18
 
0.6%
Montgomery County 18
 
0.6%
Union County 17
 
0.5%
Monroe County 17
 
0.5%
Other values (1876) 2938
93.2%

Length

2023-01-17T14:45:51.703328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
county 3007
45.9%
parish 64
 
1.0%
city 49
 
0.7%
washington 31
 
0.5%
jefferson 28
 
0.4%
franklin 26
 
0.4%
st 26
 
0.4%
lincoln 24
 
0.4%
jackson 24
 
0.4%
madison 20
 
0.3%
Other values (1875) 3250
49.6%

Most occurring characters

ValueCountFrequency (%)
n 4897
11.0%
o 4756
10.7%
t 4057
 
9.2%
u 3594
 
8.1%
C 3428
 
7.7%
y 3398
 
7.7%
3397
 
7.7%
a 2272
 
5.1%
e 2188
 
4.9%
r 1620
 
3.7%
Other values (45) 10714
24.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 34332
77.5%
Uppercase Letter 6549
 
14.8%
Space Separator 3397
 
7.7%
Other Punctuation 31
 
0.1%
Dash Punctuation 12
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 4897
14.3%
o 4756
13.9%
t 4057
11.8%
u 3594
10.5%
y 3398
9.9%
a 2272
6.6%
e 2188
6.4%
r 1620
 
4.7%
i 1282
 
3.7%
l 1277
 
3.7%
Other values (16) 4991
14.5%
Uppercase Letter
ValueCountFrequency (%)
C 3428
52.3%
M 312
 
4.8%
S 285
 
4.4%
P 266
 
4.1%
B 260
 
4.0%
W 227
 
3.5%
L 224
 
3.4%
H 199
 
3.0%
G 157
 
2.4%
D 148
 
2.3%
Other values (15) 1043
 
15.9%
Other Punctuation
ValueCountFrequency (%)
. 27
87.1%
' 4
 
12.9%
Space Separator
ValueCountFrequency (%)
3397
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 40881
92.2%
Common 3440
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 4897
12.0%
o 4756
11.6%
t 4057
9.9%
u 3594
 
8.8%
C 3428
 
8.4%
y 3398
 
8.3%
a 2272
 
5.6%
e 2188
 
5.4%
r 1620
 
4.0%
i 1282
 
3.1%
Other values (41) 9389
23.0%
Common
ValueCountFrequency (%)
3397
98.8%
. 27
 
0.8%
- 12
 
0.3%
' 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44321
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 4897
11.0%
o 4756
10.7%
t 4057
 
9.2%
u 3594
 
8.1%
C 3428
 
7.7%
y 3398
 
7.7%
3397
 
7.7%
a 2272
 
5.1%
e 2188
 
4.9%
r 1620
 
3.7%
Other values (45) 10714
24.2%

2013_urban_influence_code
Real number (ℝ)

Distinct13
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2666242
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2023-01-17T14:45:51.764971image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q38
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4942292
Coefficient of variation (CV)0.66346659
Kurtosis-1.1108671
Mean5.2666242
Median Absolute Deviation (MAD)3
Skewness0.40354753
Sum16600.4
Variance12.209638
MonotonicityNot monotonic
2023-01-17T14:45:51.819450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 735
23.3%
1 432
13.7%
6 344
10.9%
8 269
 
8.5%
5 242
 
7.7%
10 189
 
6.0%
9 184
 
5.8%
12 182
 
5.8%
7 162
 
5.1%
4 149
 
4.7%
Other values (3) 264
 
8.4%
ValueCountFrequency (%)
1 432
13.7%
2 735
23.3%
3 130
 
4.1%
4 149
 
4.7%
5 242
 
7.7%
5.266624244 9
 
0.3%
6 344
10.9%
7 162
 
5.1%
8 269
 
8.5%
9 184
 
5.8%
ValueCountFrequency (%)
12 182
5.8%
11 125
 
4.0%
10 189
6.0%
9 184
5.8%
8 269
8.5%
7 162
5.1%
6 344
10.9%
5.266624244 9
 
0.3%
5 242
7.7%
4 149
4.7%
Distinct343
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.72551
Minimum12.4
Maximum54.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2023-01-17T14:45:51.886988image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum12.4
5-th percentile22.8
Q129.075
median35.2
Q340.3
95-th percentile45.8
Maximum54.3
Range41.9
Interquartile range (IQR)11.225

Descriptive statistics

Standard deviation7.2338304
Coefficient of variation (CV)0.20831459
Kurtosis-0.68147659
Mean34.72551
Median Absolute Deviation (MAD)5.6
Skewness-0.1591878
Sum109454.81
Variance52.328302
MonotonicityNot monotonic
2023-01-17T14:45:51.958651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.9 25
 
0.8%
37.2 23
 
0.7%
33.8 22
 
0.7%
39.8 22
 
0.7%
40.9 22
 
0.7%
40.4 21
 
0.7%
40.1 21
 
0.7%
39.3 21
 
0.7%
37.8 21
 
0.7%
38.3 20
 
0.6%
Other values (333) 2934
93.1%
ValueCountFrequency (%)
12.4 1
< 0.1%
13.2 1
< 0.1%
14.2 1
< 0.1%
14.4 1
< 0.1%
15 1
< 0.1%
15.1 1
< 0.1%
15.4 1
< 0.1%
15.8 1
< 0.1%
15.9 1
< 0.1%
16.4 1
< 0.1%
ValueCountFrequency (%)
54.3 1
 
< 0.1%
52.4 1
 
< 0.1%
52 1
 
< 0.1%
51.7 2
0.1%
51.4 1
 
< 0.1%
50.9 1
 
< 0.1%
50.5 1
 
< 0.1%
50.4 2
0.1%
50.3 2
0.1%
50.1 3
0.1%
Distinct287
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.162528
Minimum9.5
Maximum44.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2023-01-17T14:45:52.032934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum9.5
5-th percentile16.955
Q122.1
median26.2
Q330.1
95-th percentile35.645
Maximum44.9
Range35.4
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.6604414
Coefficient of variation (CV)0.21635682
Kurtosis-0.33053991
Mean26.162528
Median Absolute Deviation (MAD)4
Skewness0.057527213
Sum82464.288
Variance32.040596
MonotonicityNot monotonic
2023-01-17T14:45:52.108066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.3 32
 
1.0%
27.8 31
 
1.0%
25.6 28
 
0.9%
23.3 27
 
0.9%
29 25
 
0.8%
30.4 25
 
0.8%
25.4 24
 
0.8%
28.4 24
 
0.8%
27.6 24
 
0.8%
25.3 24
 
0.8%
Other values (277) 2888
91.6%
ValueCountFrequency (%)
9.5 1
 
< 0.1%
9.9 1
 
< 0.1%
10.9 1
 
< 0.1%
11.1 1
 
< 0.1%
11.3 1
 
< 0.1%
11.8 2
0.1%
11.9 2
0.1%
12.3 1
 
< 0.1%
12.5 3
0.1%
12.6 1
 
< 0.1%
ValueCountFrequency (%)
44.9 1
 
< 0.1%
43.1 1
 
< 0.1%
42.8 1
 
< 0.1%
42.5 1
 
< 0.1%
42.4 1
 
< 0.1%
41.8 2
0.1%
41.7 1
 
< 0.1%
41.6 1
 
< 0.1%
41.3 1
 
< 0.1%
40.8 3
0.1%
Distinct2744
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22571.662
Minimum0
Maximum2241079
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2023-01-17T14:45:52.188010image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile363.2
Q11234
median3251
Q310941.25
95-th percentile104801.5
Maximum2241079
Range2241079
Interquartile range (IQR)9707.25

Descriptive statistics

Standard deviation81995.651
Coefficient of variation (CV)3.6326811
Kurtosis224.33499
Mean22571.662
Median Absolute Deviation (MAD)2520.5
Skewness11.736194
Sum71145879
Variance6.7232867 × 109
MonotonicityNot monotonic
2023-01-17T14:45:52.261781image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22571.662 10
 
0.3%
1443 5
 
0.2%
968 4
 
0.1%
649 4
 
0.1%
271 4
 
0.1%
657 4
 
0.1%
1712 4
 
0.1%
918 4
 
0.1%
312 4
 
0.1%
2078 4
 
0.1%
Other values (2734) 3105
98.5%
ValueCountFrequency (%)
0 1
< 0.1%
4 1
< 0.1%
15 1
< 0.1%
33 1
< 0.1%
35 1
< 0.1%
54 1
< 0.1%
67 1
< 0.1%
71 1
< 0.1%
74 1
< 0.1%
75 1
< 0.1%
ValueCountFrequency (%)
2241079 1
< 0.1%
1392515 1
< 0.1%
941317 1
< 0.1%
932169 1
< 0.1%
880086 1
< 0.1%
871782 1
< 0.1%
822615 1
< 0.1%
766048 1
< 0.1%
699520 1
< 0.1%
667445 1
< 0.1%
Distinct3142
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.051907
Minimum1.1169102
Maximum73.560211
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2023-01-17T14:45:52.336222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.1169102
5-th percentile5.2962737
Q18.4763753
median11.737898
Q316.648085
95-th percentile24.284482
Maximum73.560211
Range72.443301
Interquartile range (IQR)8.1717098

Descriptive statistics

Standard deviation6.2526423
Coefficient of variation (CV)0.47905965
Kurtosis4.188293
Mean13.051907
Median Absolute Deviation (MAD)3.8495517
Skewness1.3043924
Sum41139.612
Variance39.095535
MonotonicityNot monotonic
2023-01-17T14:45:52.411561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.0519075 10
 
0.3%
7.142857075 2
 
0.1%
11.48339462 1
 
< 0.1%
8.826991081 1
 
< 0.1%
10.32266235 1
 
< 0.1%
10.72658539 1
 
< 0.1%
13.03276348 1
 
< 0.1%
9.084691048 1
 
< 0.1%
12.09726143 1
 
< 0.1%
4.941589355 1
 
< 0.1%
Other values (3132) 3132
99.4%
ValueCountFrequency (%)
1.116910219 1
< 0.1%
1.142857194 1
< 0.1%
1.382863283 1
< 0.1%
1.53657043 1
< 0.1%
1.543774128 1
< 0.1%
1.869592071 1
< 0.1%
1.874332309 1
< 0.1%
1.948051929 1
< 0.1%
2.233186245 1
< 0.1%
2.311117411 1
< 0.1%
ValueCountFrequency (%)
73.56021118 1
< 0.1%
46.68651962 1
< 0.1%
46.58807373 1
< 0.1%
44.48228836 1
< 0.1%
43.07380295 1
< 0.1%
40.49938965 1
< 0.1%
40.18181992 1
< 0.1%
39.45946121 1
< 0.1%
38.43424225 1
< 0.1%
38.21749115 1
< 0.1%
Distinct3141
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.155185
Minimum7.2651358
Maximum57.433674
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2023-01-17T14:45:52.487920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum7.2651358
5-th percentile21.418785
Q129.704352
median34.504099
Q339.093508
95-th percentile45.4291
Maximum57.433674
Range50.168538
Interquartile range (IQR)9.3891559

Descriptive statistics

Standard deviation7.2215218
Coefficient of variation (CV)0.21143266
Kurtosis0.088243272
Mean34.155185
Median Absolute Deviation (MAD)4.6906891
Skewness-0.30869902
Sum107657.14
Variance52.150377
MonotonicityNot monotonic
2023-01-17T14:45:52.565518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.15518504 10
 
0.3%
40.6716423 2
 
0.1%
33.81851196 2
 
0.1%
51.79542923 1
 
< 0.1%
29.69613075 1
 
< 0.1%
41.57846451 1
 
< 0.1%
37.64207077 1
 
< 0.1%
44.48691559 1
 
< 0.1%
30.96498489 1
 
< 0.1%
41.8200531 1
 
< 0.1%
Other values (3131) 3131
99.3%
ValueCountFrequency (%)
7.265135765 1
< 0.1%
7.796900272 1
< 0.1%
8.808446884 1
< 0.1%
10.51348972 1
< 0.1%
11.60401535 1
< 0.1%
11.71112347 1
< 0.1%
12.04116535 1
< 0.1%
12.05111217 1
< 0.1%
12.3732233 1
< 0.1%
12.42215061 1
< 0.1%
ValueCountFrequency (%)
57.43367386 1
< 0.1%
56.00997543 1
< 0.1%
54.48141861 1
< 0.1%
52.22602844 1
< 0.1%
52 1
< 0.1%
51.81893158 1
< 0.1%
51.79542923 1
< 0.1%
51.78131104 1
< 0.1%
51.30740356 1
< 0.1%
51.27913666 1
< 0.1%
Distinct3141
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.818033
Minimum5.2356019
Maximum60.563381
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2023-01-17T14:45:52.648580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5.2356019
5-th percentile22.400992
Q127.302107
median30.818033
Q334.219466
95-th percentile39.431522
Maximum60.563381
Range55.327779
Interquartile range (IQR)6.9173589

Descriptive statistics

Standard deviation5.2031652
Coefficient of variation (CV)0.16883508
Kurtosis0.4143508
Mean30.818033
Median Absolute Deviation (MAD)3.4604626
Skewness-0.0070864213
Sum97138.441
Variance27.072928
MonotonicityNot monotonic
2023-01-17T14:45:52.718450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.8180334 10
 
0.3%
40.68323135 2
 
0.1%
28.45301056 2
 
0.1%
34.45205688 1
 
< 0.1%
27.92644691 1
 
< 0.1%
29.07956123 1
 
< 0.1%
30.87587547 1
 
< 0.1%
30.1684742 1
 
< 0.1%
28.6752243 1
 
< 0.1%
28.5354557 1
 
< 0.1%
Other values (3131) 3131
99.3%
ValueCountFrequency (%)
5.235601902 1
< 0.1%
11.24182701 1
< 0.1%
13.53871727 1
< 0.1%
14.06054306 1
< 0.1%
14.23705769 1
< 0.1%
14.35533524 1
< 0.1%
14.43734932 1
< 0.1%
15.54736137 1
< 0.1%
15.79799271 1
< 0.1%
16.05078697 1
< 0.1%
ValueCountFrequency (%)
60.5633812 1
< 0.1%
48.10606003 1
< 0.1%
47.33221054 1
< 0.1%
47.30671692 1
< 0.1%
46.30021286 1
< 0.1%
46.26631927 1
< 0.1%
45.6488533 1
< 0.1%
45.58437347 1
< 0.1%
45.34152985 1
< 0.1%
45.23940659 1
< 0.1%
Distinct3143
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.974874
Minimum0
Maximum77.557411
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size24.8 KiB
2023-01-17T14:45:52.792731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.149849
Q115.367731
median19.603726
Q325.989834
95-th percentile41.38832
Maximum77.557411
Range77.557411
Interquartile range (IQR)10.622102

Descriptive statistics

Standard deviation9.5588563
Coefficient of variation (CV)0.43499026
Kurtosis2.6738395
Mean21.974874
Median Absolute Deviation (MAD)4.9404383
Skewness1.4412313
Sum69264.803
Variance91.371734
MonotonicityNot monotonic
2023-01-17T14:45:52.868786image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.97487406 10
 
0.3%
26.57157326 1
 
< 0.1%
26.05515671 1
 
< 0.1%
24.88177109 1
 
< 0.1%
26.27507401 1
 
< 0.1%
17.46232414 1
 
< 0.1%
24.19367599 1
 
< 0.1%
12.53995037 1
 
< 0.1%
33.92494965 1
 
< 0.1%
13.11455536 1
 
< 0.1%
Other values (3133) 3133
99.4%
ValueCountFrequency (%)
0 1
< 0.1%
1.047120452 1
< 0.1%
3.153153181 1
< 0.1%
3.915211916 1
< 0.1%
5.386811256 1
< 0.1%
7.065526962 1
< 0.1%
7.189434528 1
< 0.1%
7.285725594 1
< 0.1%
7.490922928 1
< 0.1%
7.557436466 1
< 0.1%
ValueCountFrequency (%)
77.55741119 1
< 0.1%
75.29931641 1
< 0.1%
67.4057312 1
< 0.1%
63.09652328 1
< 0.1%
62.56293488 1
< 0.1%
62.0765686 1
< 0.1%
61.55454636 1
< 0.1%
61.34363174 1
< 0.1%
61.31498718 1
< 0.1%
60.79890823 1
< 0.1%

Interactions

2023-01-17T14:45:34.692592image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:42:26.323345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:43:45.255293image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:01.284362image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:16.464073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:32.640297image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:47.939914image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:04.315736image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:19.479697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:50.640738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:42:49.752659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:00.730034image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:15.910488image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:32.048887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:47.388901image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:03.725899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:18.924941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:34.110887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:50.710182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:42:56.469296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:00.796377image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:15.977041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:32.119709image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:47.453267image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:03.797328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:18.991385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:34.185775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:50.779789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:43:03.235087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:00.860593image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:16.040526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:32.186933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:47.519245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:03.867053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:19.055399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:34.253366image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:50.858008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:43:10.093978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:00.932005image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:16.114923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:32.265276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:47.590619image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:03.944248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:19.127122image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:34.329097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:50.928663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:43:16.642506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:00.996844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:16.183192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:32.335448image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:47.655683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:04.013104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:19.193247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:34.396263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:51.005046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:43:23.606632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:01.068044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:16.253173image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:32.412604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:47.726784image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:04.089011image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:19.265279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:34.472428image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:51.076217image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:43:31.366640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:01.133241image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:16.318052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:32.484048image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:47.793781image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:04.158573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:19.331652image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:34.540026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:51.149342image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:43:38.787742image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:01.201640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:16.384670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:32.557946image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:44:47.861322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:04.233279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:19.400410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T14:45:34.611162image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2023-01-17T14:45:52.940864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
fips2013_urban_influence_codepercent_of_adults_with_a_high_school_diploma_only_1980percent_of_adults_completing_some_college_or_associates_degree_2000bachelors_degree_or_higher_2015_19percent_of_adults_with_less_than_a_high_school_diploma_2015_19percent_of_adults_with_a_high_school_diploma_only_2015_19percent_of_adults_completing_some_college_or_associates_degree_2015_19percent_of_adults_with_a_bachelors_degree_or_higher_2015_19state
fips1.000-0.019-0.086-0.0570.002-0.015-0.022-0.0570.0541.000
2013_urban_influence_code-0.0191.0000.005-0.036-0.6780.1160.2380.134-0.3280.204
percent_of_adults_with_a_high_school_diploma_only_1980-0.0860.0051.0000.4310.072-0.5700.0890.3680.1990.342
percent_of_adults_completing_some_college_or_associates_degree_2000-0.057-0.0360.4311.0000.208-0.618-0.5880.7570.5320.323
bachelors_degree_or_higher_2015_190.002-0.6780.0720.2081.000-0.273-0.464-0.0680.5930.127
percent_of_adults_with_less_than_a_high_school_diploma_2015_19-0.0150.116-0.570-0.618-0.2731.0000.337-0.439-0.7020.249
percent_of_adults_with_a_high_school_diploma_only_2015_19-0.0220.2380.089-0.588-0.4640.3371.000-0.336-0.7320.243
percent_of_adults_completing_some_college_or_associates_degree_2015_19-0.0570.1340.3680.757-0.068-0.439-0.3361.0000.1590.284
percent_of_adults_with_a_bachelors_degree_or_higher_2015_190.054-0.3280.1990.5320.593-0.702-0.7320.1591.0000.196
state1.0000.2040.3420.3230.1270.2490.2430.2840.1961.000

Missing values

2023-01-17T14:45:51.269087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-17T14:45:51.389274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fipsstatearea_name2013_urban_influence_codepercent_of_adults_with_a_high_school_diploma_only_1980percent_of_adults_completing_some_college_or_associates_degree_2000bachelors_degree_or_higher_2015_19percent_of_adults_with_less_than_a_high_school_diploma_2015_19percent_of_adults_with_a_high_school_diploma_only_2015_19percent_of_adults_completing_some_college_or_associates_degree_2015_19percent_of_adults_with_a_bachelors_degree_or_higher_2015_19
001001ALAutauga County2.035.226.99929.011.48339533.58845928.35657126.571573
101003ALBaldwin County2.033.729.348148.09.19384327.65961631.28408131.862459
201005ALBarbour County6.026.121.32080.026.78690735.60454226.02983711.578713
301007ALBibb County1.029.620.41678.020.94260244.87877323.80009810.378526
401009ALBlount County1.032.424.85210.019.50943833.42213133.97502113.093413
501011ALBullock County6.022.517.5856.025.32037740.27601622.34896512.054640
601013ALButler County6.028.822.92230.014.95043145.18416623.72820116.137203
701015ALCalhoun County2.032.826.414620.015.56732632.79061533.16099518.481064
801017ALChambers County5.028.322.53163.018.37824236.73546631.59023113.296062
901019ALCherokee County6.028.118.92407.018.42746540.25643928.56310312.752994
fipsstatearea_name2013_urban_influence_codepercent_of_adults_with_a_high_school_diploma_only_1980percent_of_adults_completing_some_college_or_associates_degree_2000bachelors_degree_or_higher_2015_19percent_of_adults_with_less_than_a_high_school_diploma_2015_19percent_of_adults_with_a_high_school_diploma_only_2015_19percent_of_adults_completing_some_college_or_associates_degree_2015_19percent_of_adults_with_a_bachelors_degree_or_higher_2015_19
314256027WYNiobrara County12.041.933.1330.012.90322634.66375034.39037718.042646
314356029WYPark County11.042.633.67059.04.46007125.83084535.70946933.999615
314456031WYPlatte County11.045.031.21348.08.91817438.29298032.13300720.655838
314556033WYSheridan County8.038.337.26632.04.67254825.63599838.73407030.957382
314656035WYSublette County10.039.132.11551.04.00998834.63572338.57226922.782021
314756037WYSweetwater County8.043.535.76291.07.21336133.04127137.24697922.498390
314856039WYTeton County8.027.230.09875.04.81440914.87617623.30427757.005138
314956041WYUinta County8.046.834.22078.07.25856241.52267835.18975416.029003
315056043WYWashakie County11.043.233.11297.010.24161529.75117136.62098723.386225
315156045WYWeston County9.040.230.51016.06.34951837.42874136.24926419.972479